It is becoming increasingly common for shoppers to search for the particular product in which they are interested using electronic search mechanisms, such as Internet-based search engines. The complex systems used by such electronic search mechanisms to process incoming product data from multiple merchants and deliver that product data in the form of search results to millions of customers must ensure that customers receive the best information available. In order to do this, product offering information, data that represents an offer to sell a particular product by a particular party, must be obtained from multiple sources and stored in a way that allows the product offerings to be easily searched.
One approach that may be used by search mechanisms to select which product offering information to include in search results for a search query is referred to herein as the “keyword approach”. The key word approach returns those product offerings that contain the keywords contained in submitted queries. Key word searches have a problem in that they may only match data that have exact matches of the words in the product offering. This excludes similar products if they do not match the key words.
A Bayesian classifier approach overcomes the requirement of exact keyword matching by providing an estimate for the likelihood that a particular product offering satisfies a query. More details about Bayesian classifiers may be found in David D. Lewis. “Naive (Bayes) at forty: The independence assumption in information retrieval”. In Claire N'edellec and C'eline Rouveirol, editors, Proceedings of ECML-98, 10th European Conference on Machine Learning, number 1398, pages 4-15, Chemnitz, DE, 1998. Springer Verlag, Heidelberg, Del.
In general, Bayesian classifiers are, mathematically speaking, a network of interconnected nodes that are trained on known data (product offering data with known classifications) to predict the likelihood that an input product offering is associated with a given output classification. A problem with the Bayesian classifier approach is that it does not utilize the interrelationships among disparate product offerings for the same or similar products from one or more merchants.
In another approach, the product association approach, multiple product offerings for the same product are grouped together once the data for those product offerings is retrieved. This approach simplifies query response preparation in that it utilizes the interrelationships among disparate product offerings for the same product from one or more merchants. The product association approach has a problem, however, in that it does not utilize the interrelationships among disparate product offerings for similar, non-identical products from one or more merchants. Consider, for example, a merchant A offering to sell a signed, first edition copy of Fitzgerald's “The Great Gatsby” (“A's Gatsby product offering”). A product association approach would associate A's Gatsby product offering with a product offering for a signed, first edition copy of “The Great Gatsby” from merchant B, but would not, however, associate A's Gatsby product offering with merchant C's product offering of a signed, first edition copy of Fitzgerald's “Tender is the Night”.
Therefore, based on the foregoing, it is clearly desirable to provide a mechanism that overcomes the problems of needing exact matches of key words from the query, not utilizing the interrelationships among product offerings for the same product from multiple merchants, and not utilizing the interrelationships among product offerings for the similar products from the same or different merchants.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.